What you'll learn

Introduction to Deep Learning: Understand the definition, role, and components of Deep Learning in AI.

Applications of Deep Learning: Explore real-world applications like healthcare, finance, retail, and autonomous systems.

Artificial Neural Networks (ANN): Learn the structure and functioning of ANNs with input, hidden, and output layers.

Backpropagation: Master how backpropagation optimizes neural networks through gradient descent.

Applications of ANN: Apply ANN to tasks like image classification, NLP, and predictive modeling.

Convolutional Neural Networks (CNN): Dive into CNN architecture for analyzing image data effectively.

Applications of CNN: Use CNNs for face recognition, medical imaging, and autonomous vehicle systems.

Advanced CNN Concepts: Study techniques like padding, stride, and dropout to enhance CNN performance.

Recurrent Neural Networks (RNN): Understand RNNs for modeling sequential data with temporal dependencies.

Vanishing and Exploding Gradients: Learn solutions to gradient problems, like LSTMs and GRUs.

Applications of RNN: Use RNNs in language modeling, time-series forecasting, and speech recognition.

Long Short-Term Memory (LSTM): Discover how LSTMs solve sequential learning challenges using memory gates.

Applications of LSTM: Apply LSTMs to tasks like sentiment analysis and predictive maintenance.

Gated Recurrent Unit (GRU): Understand GRUs for simpler and efficient sequential data modeling.

GANs (Generative Adversarial Networks): Explore GANs for generating synthetic data and creative applications.

Transfer Learning: Reduce training time by leveraging pre-trained models for specific tasks.

Pre-trained Models: Use models like VGG and ResNet for feature extraction and fine-tuning.

Evaluation Metrics: Evaluate models using metrics like accuracy, precision, recall, and F1-score.

Loss Functions: Learn loss functions like cross-entropy for classification and MSE for regression.

Computer Vision Basics: Study how AI processes and analyzes visual data for insights.

Deep Learning in Computer Vision: Implement CNNs for tasks like image segmentation and detection.

Object Detection: Apply YOLO, SSD, and Faster R-CNN for real-time object detection.

Facial Recognition: Explore algorithms for face detection, analysis, and recognition systems.

Motion Analysis and Tracking: Track objects and analyze motion using techniques like optical flow.

3D Vision: Reconstruct 3D structures and enable depth perception from 2D images.

Applications of Computer Vision: Implement vision solutions in healthcare, retail, security, and AR/VR.

Requirements

This masterclass is designed for everyone—no prior experience is required, as the concepts are explained in a simple and accessible manner.